Myogram determination from ecg signal

ABSTRACT

Systems and methods for measuring signals representative of muscle activity are provided. One method includes detecting an ECG signal through a plurality of electrodes. The ECG signal includes a plurality of ECG sample signals, and each ECG sample signal is a bipolar signal associated with two of the plurality of electrodes and includes a cardiac signal component and a myographic signal component. The method further includes filtering each of the ECG sample signals to remove at least a portion of the cardiac signal component and generate a combined myographic power signal for the two of the plurality of electrodes with which the ECG sample signal is associated. Each combined myographic power signal represents a myographic potential between the two electrodes. The method further includes calculating individual myographic power signals for each of the plurality of electrodes by applying the combined myographic power signals within a covariance matrix.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. patent application Ser. No.14/196,962, filed Mar. 4, 2014, which is hereby incorporated byreference in its entirety.

BACKGROUND

The present disclosure relates generally to the field of biologicalsignal measurement. More particularly, the present disclosure relates tosystems of and methods for measuring myographic signals from capturedECG signals.

Assessment of a patient's respiratory condition may be useful for avariety of purposes. For example, a patient's respiratory condition maybe evaluated in clinical environments to confirm whether there is a riskto the patient's respiratory health or function. In someimplementations, information regarding a patient's respiratory conditionmay be used in performing related operations, such as capturing anelectrocardiogram (ECG) signal. For example, because respiratory-relatedmuscle movements can cause body shifts (e.g., heart movements) that canaffect captured ECG signals, respiratory-related information may beuseful in ensuring that ECG signals are captured during periods of lessrespiratory movement. There is a need for a system that can extractmuscle activity information, such as respiratory information, from ECGsignals and is able to isolate the components of the muscle activityinformation associated with individual electrodes.

SUMMARY

One embodiment of the disclosure relates to a method of measuringsignals representative of muscle activity. The method includesdetecting, using a processing circuit, an electrocardiogram (ECG) signalthrough a plurality of electrodes. The ECG signal includes a pluralityof ECG sample signals, and each ECG sample signal is a bipolar signalassociated with two of the plurality of electrodes and includes acardiac signal component and a myographic signal componentrepresentative of muscle contractions relating to the muscle activity.The method further includes filtering, using the processing circuit,each of the ECG sample signals to remove at least a portion of thecardiac signal component and generate a combined myographic power signalfor the two of the plurality of electrodes with which the ECG samplesignal is associated. Each combined myographic power signal represents amyographic potential between the two electrodes. The method furtherincludes calculating, using the processing circuit, individualmyographic power signals for each of the plurality of electrodes byapplying the combined myographic power signals within a covariancematrix.

Another embodiment relates to a system for measuring signalsrepresentative of muscle activity. The system includes a circuitconfigured to detect an electrocardiogram (ECG) signal through aplurality of electrodes. The ECG signal includes a plurality of ECGsample signals, and each ECG sample signal is a bipolar signalassociated with two of the plurality of electrodes and comprises acardiac signal component and a myographic signal componentrepresentative of muscle contractions relating to the muscle activity.The circuit is further configured to filter each of the ECG samplesignals to remove at least a portion of the cardiac signal component andgenerate a combined myographic power signal for the two of the pluralityof electrodes with which the ECG sample signal is associated. Eachcombined myographic power signal represents a myographic potentialbetween the two electrodes. The circuit is further configured tocalculate individual myographic power signals for each of the pluralityof electrodes by applying the combined myographic power signals within acovariance matrix.

Another embodiment relates to a system for measuring signalsrepresentative of respiratory activity. The system includes a pluralityof electrodes configured to capture an electrocardiogram (ECG) signal.The ECG signal includes a plurality of ECG sample signals, and each ECGsample signal is a bipolar signal associated with two of the pluralityof electrodes and comprises a cardiac signal component and a myographicsignal component representative of muscle contractions relating to therespiratory activity. The system further includes a circuit operablycoupled to the plurality of electrodes and including a microprocessor.The circuit is configured to designate one of the plurality ofelectrodes to be a reference electrode and the others of the pluralityof electrodes to be recording electrodes. The circuit is furtherconfigured to detect a first set of ECG sample signals within the ECGsignal. The first set of ECG sample signals comprises an ECG samplesignal for each recording electrode with respect to the referenceelectrode. The circuit is further configured to filter each of the firstset of ECG sample signals to remove at least a portion of the cardiacsignal component and generate a first set of combined myographic powersignals. The first set of combined myographic power signals comprises acombined myographic power signal for each recording electrode withrespect to the reference electrode. The circuit is further configured tocalculate an individual myographic power signal for the referenceelectrode by applying the first set of combined myographic power signalswithin a covariance matrix. The circuit is further configured tocalculate individual myographic power signals for each of the pluralityof electrodes other than the reference electrode by: (1) changing thereference electrode to another of the plurality of electrodes todesignate a new reference electrode; (2) designating the electrodes ofthe plurality of electrodes other than the new reference electrode to benew recording electrodes; (3) determining a second set of combinedmyographic power signals including a combined myographic power signalfor each new recording electrode with respect to the new referenceelectrode; (4) calculating the individual myographic power signal forthe new reference electrode by applying the second set of combinedmyographic power signals within the covariance matrix; and (5) repeatingthe changing, designating, determining, and calculating operations untilthe individual myographic power signals have been calculated for each ofthe plurality of electrodes.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure will become more fully understood from the followingdetailed description, taken in conjunction with the accompanyingfigures, wherein like reference numerals refer to like elements, inwhich:

FIG. 1 is a block diagram of a system for measuring signalsrepresentative of respiratory activity according to an exemplaryembodiment;

FIG. 2 is a flow diagram of a process calculating signals representativeof respiratory activity according to an exemplary embodiment;

FIG. 3 is a flow diagram of a more detailed process for calculatingsignals representative of respiratory activity that are specific toindividual electrodes to an exemplary embodiment;

FIG. 4 is a graph of a frequency response for an illustrative high passfilter according to an exemplary embodiment; and

FIG. 5 is a graph of an estimated myographic signal for a particularelectrode over a sampling time period according to an exemplaryembodiment.

DETAILED DESCRIPTION

Before turning to the figures, which illustrate the exemplaryembodiments in detail, it should be understood that the application isnot limited to the details or methodology set forth in the descriptionor illustrated in the figures. It should also be understood that theterminology is for the purpose of description only and should not beregarded as limiting.

Referring generally to the figures, systems and methods that may be usedto measure and/or calculate myographic signals representative of muscleactivity (e.g., respiratory activity) from captured electrocardiogram(ECG) signals are provided according to exemplary embodiments. Systemsconfigured to measure myographic signals representative of musclecontractions resulting from respiratory movements (e.g., movements ofthe diaphragm and/or intercostal muscles) often use separate devicesdedicated to measuring respiratory function. For example, such systemsmay utilize air flow sensors, mechanical movement sensors, and/or othertypes of sensors to measure air flow and/or body movement resulting fromrespiratory activity. Such sensors may provide for effective measurementof respiratory function, but separate sensors result in added expenseand complexity for the system to achieve respiratory monitoring.

Some ECG systems may be configured to monitor some respiratory activityby processing myographic signals from within captured ECG signals. Suchsystems may avoid the expense and complexity of utilizing dedicatedsensors for measuring respiratory information. Such systems measuremyographic data for a combination of electrodes. For example, thesystems may measure bipolar myographic potentials between twoelectrodes. Such bipolar myographic potentials may represent adifference in myographic potentials between the two electrodes. Unlessthe two electrodes are very close together, the myographic potentials ateach electrode are independent, and the myographic power calculatedusing the bipolar signals may be a combination (e.g., sum) of themyographic power signals occurring at each electrode. Thus, such systemscannot distinguish between the components of the combined myographicpower signal attributable to each individual electrode.

The systems and methods of the present disclosure are configured tocalculate myographic signals from ECG signals in a manner in which themyographic signals may be isolated to individual electrodes. Anexemplary system may detect an ECG signal that includes several ECGsample signals sampled over a sampling timeframe. Each sample signal maybe a bipolar signal associated with two electrodes, and may include acardiac signal component and a myographic signal componentrepresentative of muscle contractions relating to respiratory activity.The system may filter the ECG sample signals to remove at least aportion of the cardiac signal components and generate combined (e.g.,bipolar) myographic power signals. Each combined myographic power signalrepresents a myographic potential between two electrodes. In someembodiments, the filtering may include high pass filtering to removelarge low frequency cardiac signal components of the ECG sample signals.In some embodiments, the filtering may additionally or alternatively beconfigured to remove at least a portion of the ECG sample signals at afrequency at or near 50 Hz and/or 60 Hz (e.g., remove alternatingcurrent (AC) interference).

The system may apply the combined myographic power signals resultingfrom the filtering in a covariance matrix, and may calculateindividualized myographic power signals for each electrode using thecovariance matrix. For example, in some embodiments, the system mayinitially designate one electrode as a reference electrode and the otherelectrodes as recording electrodes. Bipolar ECG sample signals may becollected by the system for each recording electrode with respect to thereference electrode. The bipolar ECG sample signals may be filtered toremove at least a portion of the cardiac components and generate bipolarmyographic power signals. The system may then utilize a covariancematrix that includes the bipolar myographic power signals to calculatean individual myographic power signal associated with the referenceelectrode. For example, the individual myographic power signal for thereference electrode may be calculated based on one or more off-diagonalelements in the covariance matrix. The reference electrode then may bechanged to another electrode, bipolar myographic power signals may berecalculated for the new recording electrodes with respect to the newreference electrode, and an individual myographic power signal may becalculated by applying the new bipolar myographic power signals withinthe covariance matrix. The system may repeat this process until anindividual myographic power signal has been calculated for eachelectrode, or for all electrodes for which an individual myographicpower estimate is desired.

Determining myographic power signals associated with individualelectrodes may provide one or more of various advantages. For example,the location of electrodes is often determined by the exigencies of theECG recording. If the only available myographic power estimates are frompairs of electrodes, it is likely that some members of the pairs havesubstantially different myographic power than their opposite member. Thedifference may be not only in the magnitude of the myographic power, butalso in its nature. For example, an electrode on the arm or leg mightrecord peripheral muscle activity unrelated to respiration, while anelectrode on the chest will be more sensitive to intercostal muscleactivity related to respiration. If only the combined/average myographicpower is available, one type of activity may mask the other. In someembodiments, for example, capturing the myographic power from individualelectrodes may permit the identification of optimized estimates of aparticular activity such as respiration.

While the exemplary embodiments presented herein are described primarilywith respect to muscle activity related to respiratory function, thesystems and methods discussed herein may be utilized in detectingmyographic signals relating to other types of muscle activity and/or forother purposed. For example, extracted myographic signals may beutilized to differentiate diaphragm-based breathing from chest/ribeffort, depending on knowledge of the electrode placements. In anotherexemplary embodiment, the extracted myographic signal may be used todifferentiate between leg and arm movements, where systems configured todetermine a myographic signal associated with a combination ofelectrodes may only be able to determine that the myographic activity isassociated with a combination of the leg and arm electrodes. In someembodiments, the extracted myographic signals may be used to optimizeselection of ECG bipolar leads with reduced noise, which may yield moreaccurate amplitude and/or ECG landmark detection. All such embodimentsare contemplated within the scope of the present disclosure.

Referring now to FIG. 1, a block diagram of a system 100 including ameasurement circuit 105 configured to measure an ECG and calculateelectrode-specific myographic signals is shown according to an exemplaryembodiment. Circuit 105 may include a processor 120, which may be anygeneral purpose or special purpose processor (e.g., FPGA, CPLD, ASIC,etc.). Circuit 105 may also include a memory 130, which may include anycomputer readable storage medium (e.g., RAM, ROM, PROM, hard disk,optical storage, flash storage, etc.).

Circuit 105 may be configured to detect ECG signals through two or moreelectrodes 110 configured to measure electrical signals through contactwith the patient's skin. Electrodes 110 may be coupled to circuit 105through an input interface 150, which may be any wired or wirelessinterface suitable for communicatively coupling electrodes 110 tocircuit 105. Memory 130 may include an ECG processing module 135 (e.g.,instructions configured to be executed by processor 120) configured toreceive input signals from electrodes 110 and to perform processing togenerate the ECG signal. In some embodiments, ECG processing module 135may process the ECG signal into a textual and/or graphical formatsuitable for presentation to a user and transmit the resultant data to adisplay device 115 (e.g., any display device, such as a CRT, LCD, LED,etc.) via an output interface 155 (e.g., any wired and/or wirelessinterface, such as a USB interface, WiFi interface, Bluetooth interface,serial port, etc.). In some embodiments, ECG processing module 135 mayprocess the ECG signal to identify one or more signal components withinthe signal, such as a P wave and/or QRS wave within the ECG signal.

Memory 130 may also include a myogram calculation module 140 configuredto extract myographic components from the ECG signal and calculate amyographic signal for one or more of electrodes 110. The captured ECGsignal may include both cardiac components related to the function ofthe heart and myographic components related to respiratory activity. Forexample, the myographic components may be electrical potentialsgenerated by the diaphragm and intercostal muscles responsible forrespiratory function. Myogram calculation module 140 may be configuredto isolate the myographic components of the ECG signal and remove atleast some of the cardiac components. Myogram calculation module 140 maythen perform calculations on the remaining ECG signal components todetermine myographic power signals associated with individual ones ofelectrodes 110. In some embodiments, myogram calculation module 140 maybe configured to generate a textual and/or graphical representation ofthe individualized myographic power signals for each electrode 110 andtransmit the information to display device 115 for presentation to auser.

FIG. 2 illustrates a flow diagram of a process 200 for calculatingindividual myographic power signals from and ECG signal according to anexemplary embodiment. Referring now to both FIGS. 1 and 2, circuit 105may be configured to detect the ECG signal using electrodes 110 (205).The ECG signal may include several ECG sample signals or sequencessampled at a particular sampling rate. The ECG sample signals may bebipolar signals, or relative signals representative of a measureddifference (e.g., electrical potential difference) between twoelectrodes 110. Each ECG sample signal may include a cardiac componentand a myographic component representative of muscle contractionsrelating to the respiratory activity.

Circuit 105 may be configured to filter each of the ECG sample signalsto remove at least a portion of the cardiac signal component and/oremphasize a portion of the myographic component and generate a combinedmyographic power signal (210). The combined myographic power signal mayrepresent a myographic potential between the two electrodes with whichthe combined myographic power signal is associated. A myogram is a widespectrum, low amplitude signal often not visible in an ECG at normalamplification. The ECG signal has large low frequency componentsdistributed largely throughout the sample sequence. Circuit 105 may beconfigured to focus on recovery of the high frequency components of themyogram and remove these large low frequency ECG signal components. Forexample, circuit 105 may remove the low frequency components using ahigh pass filter (e.g., a hardware and/or software signal filter). Insome embodiments, circuit 105 may further filter the ECG sample signalsto remove at least some remaining high frequency cardiac components. Forexample, circuit 105 may identify one or more high frequency componentspresent in the ECG sample signals (e.g., one or more QRS waves and/or Pwaves) and replace components of the ECG sample signals at the locationof the identified QRS and/or P waves with corresponding components fromprevious samples that did not include the QRS and/or P waves. In someembodiments, circuit 105 may additionally or alternatively filter theECG sample signals to remove frequency components corresponding tofrequencies of 50 Hz and/or 60 Hz (e.g., remove AC interference/noise).Once the filtering has been completed, circuit 105 may obtain thecombined myographic power signal for each ECG sample signal.

Circuit 105 may be configured to calculate individual myographic powersignals for each electrode 110 by applying the combined myographic powersignals within a covariance matrix (215). Circuit 105 may be configuredto compute one or more covariance matrix elements using the combined(e.g., bipolar) myographic power signals. In some implementations,circuit 105 may calculate individual myographic power signals for aparticular electrode by designating that electrode as a reference,determining the combined myographic power signals for the otherelectrodes with respect to the reference electrode, and calculating theindividual myographic power signal based on one or more off-diagonalelements of the covariance matrix.

FIG. 3 illustrates a more detailed flow diagram of a process 300 forcalculating individual myographic power signals for different electrodesaccording to an exemplary embodiment. Referring to both FIGS. 1 and 3,circuit 105 may initially designate one of electrodes 110 as a referenceelectrode and the other electrodes 110 as recording electrodes (305).Circuit 105 may detect an ECG signal including a first set of ECG samplesignals (310). The first set of ECG sample signals may include a samplesequence for each recording electrode with respect to the referenceelectrode. In some embodiments, a sample sequence y(i) may be obtainedfor each of the bipolar signals to be used in the covariance matrixcalculation (e.g., for each recording electrode with respect to thereference electrode), wherein “i” designates a sample number. In someembodiments, each sample sequence y(i) may include a predeterminedsample rate, such as 1000 samples/second. In one exemplary embodiment inwhich the system includes three electrodes C0, C1, and C2, where C0 isinitially designated as the reference electrode, the ECG signal mayinclude sample sequence y₁(i) measured between recording electrode C1and reference electrode C0, and sample sequence y₂(i) measured betweenrecording electrode C2 and reference electrode C0.

Circuit 105 may filter each of the first set of ECG sample signals toremove at least a portion of the cardiac components of the signals andgenerate a first set of combined (e.g., bipolar) myographic powersignals (315). In some embodiments, circuit 105 may apply a low passfilter to the ECG sample signals to generate a low pass filteredsequence. One such low pass filter may be defined as follows:

ylow(i)=y(i)+y(i+1)+y(i+2)

The low pass filter may be applied to each bipolar sample signal togenerate ylow₁(i) and ylow₂(i) low pass filtered signals for electrodesC1 and C2, respectively, with reference to reference electrode C0. Insome embodiments, because the myographic signal may be very low inamplitude, the highest frequencies in a particular sequence (e.g., a1000 s/s sequence) may have broad spectrum noise content from theacquisition signal that exceeds the myographic signal components (e.g.,quantization noise from the analog-to-digital conversion process). Insome such embodiments, low pass filtering may be used to discardfrequencies above a particular level, such as 200 Hz.

The sample signals may additionally or alternatively be applied to ahigh pass filter configured to remove low frequency components of theECG signal. One suitable high pass filter may be the third differenceover 18 milliseconds, and may be represented by the following:

yhigh(i)=ylow(i)−3*ylow(i+6)+3*ylow(i+12)−ylow(i+18)

This filter provides a −18 dB/octave roll off of low frequencies with a−3 dB roll off at approximately 50 Hz. Such a filter is effective atremoving most of the ECG cardiac signal components apart from highfrequency components of the P and QRS waves. The high pass filter may beapplied to generate high pass filtered sample signals for each recordingelectrode with respect to the reference electrode, yhigh₁(i) andyhigh₂(i).

The amplitudes of the remaining high frequency components of the ECG mayreadily exceed those of the myographic components. To account for this,circuit 105 may be configured to determine the location of the P and/orQRS waves within the filtered sample sequences. In some embodiments, thelocation of the P and/or QRS waves may be determined by ECG processingmodule 135 using a conventional method of detecting and identifying Pand/or QRS waves within an ECG signal. If any of the samples y(i)included in the calculation of yhigh(i) are within a P wave and/or QRSwave of the ECG signal, the value of yhigh(i) may be replaced by adifferent sample. For example, yhigh(i) may be replaced by yhigh(i-100)(e.g., if the P wave and/or QRS wave is expected to have a durationunder 100 milliseconds). In some circumstances yhigh(i-100) may havealready been replaced by yhigh(i-200), and so on. In some embodiments, adifferent duration may be chosen, such as 200 milliseconds. Theresultant filtered signal may be an estimation of the sample sequenceswithout the presence of the P and/or QRS waves.

In some embodiments, the samples may be further filtered to reduced ACinterference, such as frequency components at 50 Hz, 60 Hz, and/or theirharmonics that may serve to mask the myographic signal components. Sucha filter may define a further filtered sequencez(i)=yhigh(i)−yhigh(i-100) in cases where yhigh(i) has not been replaceddue to overlapping P and/or QRS waves. When yhigh(i) has been replaced,the sequence may be defined as z(i)=z(i-100). This method may be used tocalculate z₁ and z₂ for recording electrodes C1 and C2, respectively,with reference to reference electrode C0. In implementations in whichmyographic power is more desired than amplitude, circuit 105 may beconfigured to calculate the average value of the square of z(i) over aninterval of time (e.g., 100 milliseconds).

FIG. 4 illustrates a graph 400 showing a frequency response of oneillustrative filter to which sample sequences may be applied accordingto an exemplary embodiment. Graph 400 may correspond to the frequencyresponse of filters yhigh(i) and/or z(i) discussed above. It can be seenin graph 400 that there is a null response at approximately 50 Hz and 60Hz, which may be designed to prevent AC interference present in the rawsample sequence y(i) from being present as additive noise in themyographic signal represented by z(i).

Referring again to FIGS. 1 and 3, circuit 105 may calculate anindividual myographic power signal for the reference electrode C0 byapplying the first set of combined myographic power signals within acovariance matrix (320). The combined (e.g., bipolar) myographicpotential z_(k) for a particular electrode k may be represented asfollows, where e_(k) is the myographic potential associated withelectrode k and e₀ is the myographic potential associated with referenceelectrode C0:

z _(k)(i)=e _(k)(i)−e₀(i)

A covariance matrix M[j,k] can be calculated for z(i), which may bedefined as the average product of z_(j)(i)*z_(k)(i) over a particularsampling period (e.g., 100 milliseconds, including 100 samples at a rateof 1000 samples/second). The covariance matrix M[j,k] may be calculatedas follows:

$\begin{matrix}{{M\left\lbrack {j,k} \right\rbrack} = {\langle{{z_{j}(i)}*{z_{k}(i)}}\rangle}} \\{= {\langle{\left( {{e_{j}(i)} - {e_{0}(i)}} \right)*\left( {{e_{k}(i)} - {e_{0}(i)}} \right)}\rangle}} \\{= {{\langle{{e_{j}(i)}*{e_{k}(i)}}\rangle} - {\langle{{e_{j}(i)}*{e_{0}(i)}}\rangle} - {\langle{{e_{k}(i)}*{e_{0}(i)}}\rangle} + {\langle{{e_{0}(i)}*{e_{0}(i)}}\rangle}}}\end{matrix}$

When j and k are not equal, corresponding to the off diagonal elementsof the covariance matrix, the first three terms in the last equationabove are each, on average, zero, based on the assumption that e_(k)(i)contains only myographic potentials after the cardiac components havebeen filtered out of the ECG sample signals, and that the myographicpotentials at different electrodes are uncorrelated. Under theseassumptions, each of the off diagonal elements of the covariance matrixprovides an estimate of the myographic power at electrode C0,<e₀(i)*e₀(i)>. The off diagonal elements may be averaged to provide anestimate of the myographic power associated with electrode C0. In someembodiments, a covariance matrix may be calculated for each of severalsample sequence blocks (e.g., containing 100 samples, or 100milliseconds), and the resultant values for different blocks may providea sequence of myographic power values specific to electrode C0.

FIG. 5 illustrates a graph 500 showing an estimated myographic powersignal associated with a single electrode over a period of 60 secondsaccording to an exemplary embodiment. Graph 500 illustrates the periodicbehavior of the respiratory activity measured in the area near thesingle electrode.

Referring again to FIGS. 1 and 3, circuit 105 may be configured tocalculate individual myographic power signals for each electrode 110other than the current reference electrode (325). For example, in thethree electrode system noted above, circuit 105 may be configured tocalculate individual myographic power signals for electrodes C1 and C2as well. Circuit 105 may be configured to change the reference electrodeto designate a new reference electrode (330), such as electrode Cl, anddesignate the remaining electrodes as new recording electrodes (335).Circuit 105 may then determine a second set of combined myographic powersignals for the new recording electrodes C0 and C2 with respect to newreference electrode C1 (340). The second set of combined myographicpower signals may be determined using the originally captured andprocessed combined myographic power signals. For example, to calculatethe new sample sequences when the reference is changed from C0 to Cl,the value of z₁(i)=e₁(i)−e₀(i) may be subtracted from the originalsamples, which converts

z _(2-original)(i)=e ₂(i)−e₀(i)

to

z _(2-new)(i)=e ₂(i)−e₁(i).

The negated value of z₁(i) may become the second bipolar myographicsignal referenced to C1 (e.g., z_(0-new)(i)=−z_(1-original)(i)). Thesecond set of combined myographic power signals with reference to newreference electrode C1 may be applied to one or more covariance matricesto determine an individual myographic power signal for electrode C1 in amanner similar to that noted above (345). Circuit 105 may repeatoperations 330-345 until the individual myographic power signals havebeen calculated for each electrode 110 for which individual myographicpower signals are desired (350).

While the discussion above focuses on an example including threeelectrodes, it should be appreciated that the methods described may beapplied to a system with any number of electrodes. For example, if asystem having four electrodes is used, additional off diagonalcovariance matrix elements (e.g., elements M[1,2], M[1,3], and M[2,3])may be calculated to estimate the individual myographic power signal ata particular electrode. In some implementations, the off diagonalelements may be combined (e.g., averaged) to generate the individualizedestimates. In some embodiments, the additional off diagonal elements maybe used to test the validity of the assumption that the myographic noiseis uncorrelated between electrodes. If the assumption is valid, each ofthe off diagonal elements should be approximately equal.

The disclosure is described above with reference to drawings. Thesedrawings illustrate certain details of specific embodiments thatimplement the systems and methods of the present disclosure. However,describing the disclosure with drawings should not be construed asimposing on the disclosure any limitations that may be present in thedrawings. The present disclosure contemplates methods, systems andprogram products on any machine-readable media for accomplishing itsoperations. The embodiments of the present disclosure may be implementedusing an existing computer processor, or by a special purpose computerprocessor incorporated for this or another purpose or by a hardwiredsystem. No claim element herein is to be construed under the provisionsof 35 U.S.C. §112, sixth paragraph, unless the element is expresslyrecited using the phrase “means for.” Furthermore, no element, componentor method step in the present disclosure is intended to be dedicated tothe public, regardless of whether the element, component or method stepis explicitly recited in the claims.

Embodiments within the scope of the present disclosure include programproducts comprising machine-readable storage media for carrying orhaving machine-executable instructions or data structures storedthereon. Such machine-readable storage media can be any available mediawhich can be accessed by a general purpose or special purpose computeror other machine with a processor. By way of example, suchmachine-readable storage media can include RAM, ROM, EPROM, EEPROM, CDROM or other optical disk storage, magnetic disk storage or othermagnetic storage devices, or any other medium which can be used to carryor store desired program code in the form of machine-executableinstructions or data structures and which can be accessed by a generalpurpose or special purpose computer or other machine with a processor.Combinations of the above are also included within the scope ofmachine-readable storage media. Machine-executable instructions include,for example, instructions and data which cause a general purposecomputer, special purpose computer, or special purpose processingmachine to perform a certain function or group of functions. Machine orcomputer-readable storage media, as referenced herein, do not includetransitory media (i.e., signals in space).

Embodiments of the disclosure are described in the general context ofmethod steps which may be implemented in one embodiment by a programproduct including machine-executable instructions, such as program code,for example, in the form of program modules executed by machines innetworked environments. Generally, program modules include routines,programs, objects, components, data structures, etc., that performparticular tasks or implement particular abstract data types.Machine-executable instructions, associated data structures, and programmodules represent examples of program code for executing steps of themethods disclosed herein. The particular sequence of such executableinstructions or associated data structures represent examples ofcorresponding acts for implementing the functions described in suchsteps.

An exemplary system for implementing the overall system or portions ofthe disclosure might include a general purpose computing device in theform of a computer, including a processing unit, a system memory, and asystem bus that couples various system components including the systemmemory to the processing unit. The system memory may include read onlymemory (ROM) and random access memory (RAM) or other storage medium. Thecomputer may also include a magnetic hard disk drive for reading fromand writing to a magnetic hard disk, a magnetic disk drive for readingfrom or writing to a removable magnetic disk, and an optical disk drivefor reading from or writing to a removable optical disk such as a CD ROMor other optical media. The drives and their associated machine-readablemedia provide nonvolatile storage of machine-executable instructions,data structures, program modules, and other data for the computer. Acomputer readable storage medium, as referenced herein, is tangible andnon-transitory (i.e., does not include mere signals in space).

It should be noted that although the flowcharts provided herein show aspecific order of method steps, it is understood that the order of thesesteps may differ from what is depicted. Also, two or more steps may beperformed concurrently or with partial concurrence. Such variation willdepend on the software and hardware systems chosen and on designerchoice. It is understood that all such variations are within the scopeof the disclosure. Likewise, software and web implementations of thepresent disclosure could be accomplished with standard programmingtechniques with rule-based logic and other logic to accomplish thevarious database searching steps, correlation steps, comparison stepsand decision steps. It should also be noted that the word “component” asused herein and in the claims is intended to encompass implementationsusing one or more lines of software code, and/or hardwareimplementations, and/or equipment for receiving manual inputs.

The foregoing description of embodiments of the disclosure have beenpresented for purposes of illustration and description. It is notintended to be exhaustive or to limit the disclosure to the precise formdisclosed, and modifications and variations are possible in light of theabove teachings or may be acquired from practice of the disclosure. Theembodiments were chosen and described in order to explain the principalsof the disclosure and its practical application to enable one skilled inthe art to utilize the disclosure in various embodiments and withvarious modifications as are suited to the particular use contemplated.

What is claimed is:
 1. A method of measuring signals representative ofrespiratory activity, the method comprising: detecting, using aprocessing circuit, an electrocardiogram (ECG) signal through aplurality of electrodes, wherein the ECG signal comprises a plurality ofECG sample signals, wherein each ECG signal is a bipolar signalassociated with two of the plurality of electrodes and comprises acardiac signal component and a myographic signal componentrepresentative of muscle contractions relating to the respiratoryactivity; filtering, using the processing circuit, each of the ECGsample signals to remove at least a portion of the cardiac signalcomponent and generate a combined myographic power signal for the two ofthe plurality of electrodes with which the ECG sample signal isassociated, wherein each combined myographic power signal represents amyographic potential between the two electrodes; and calculating, usingthe processing circuit, individual myographic power signals for each ofthe plurality of electrodes by applying the combined myographic powersignals within a covariance matrix; wherein: the method furthercomprises designating one of the plurality of electrodes to be areference electrode and the others of the plurality of electrodes to berecording electrodes; detecting the ECG signal comprises detecting afirst set of ECG sample signals comprising an ECG sample signal forreach recording electrode with respect to the reference electrode;filtering each of the ECG sample signals comprises filtering each of thefirst set of ECG sample signals to generate a first set of combinedmyographic power signals, wherein the first set of combined myographicpower signals comprises a combined myographic power signal for eachrecording electrode with respect to the reference electrode; andcalculating the individual myographic power signals comprises applyingthe first set of combined myographic power signals within the covariancematrix and calculating the individual myographic power signal for thereference electrode.
 2. The method of claim 1, wherein the individualmyographic power signal for the reference electrode is calculated basedon one or more off-diagonal combined myographic power signals within thecovariance matrix.
 3. The method of claim 1, further comprising:changing the reference electrode to another of the plurality ofelectrodes to designate a new reference electrode; designating theelectrodes of the plurality of electrodes other than the new referenceelectrode to be new recording electrodes; determining a second set ofcombined myographic power signals comprising a combined myographic powersignal for each new recording electrode with respect to the newreference electrode; calculating the individual myographic power signalfor the new reference electrode by applying the second set of combinedmyographic power signals within the covariance matrix.
 4. The method ofclaim 3, further comprising repeating the changing, designating,determining, and calculating operations until the individual myographicpower signals have been calculated for each of the plurality ofelectrodes.
 5. The method of claim 1, wherein filtering each of the ECGsample signals to generate a combined myographic power signal for thetwo of the plurality of electrode with which the ECG sample signal isassociated comprises applying a high pass filter to the ECG samplesignals to remove low frequency components of the ECG sample signals. 6.The method of claim 5, wherein filtering each of the ECG sample signalsto generate a combined myographic power signal for the two of theplurality of electrodes with which the ECG sample single is associatedfurther comprises: determining a presence of at least one of a QRS waveof a P wave within a portion of the first set of ECG sample signals;replacing the portion of the first set of ECG sample signals includingthe at least one of the QRS wave of the P wave with a correspondingportion of the first set of ECG sample signals from a previouslydetected sample.
 7. The method of claim 5, wherein filtering each of theECG sample signals to generate a combined myographic power signal forthe two of the plurality of electrodes with which the ECG sample signalis associated further comprises filtering out at least a portion of theECG samples signals corresponding to a frequency of at least one of 50Hz or 60 Hz.
 8. A system for measuring signals representative ofrespiratory activity, the system comprising: a circuit configured to:detect an electrocardiogram (ECG) signal through a plurality ofelectrodes, wherein the ECG signal comprises a plurality of ECG samplesignals, wherein each ECG sample signal is a bipolar signal associatedwith two of the plurality of electrodes and comprises a cardiac signalcomponent and a myographic signal component representative of musclecontractions relating to the respiratory activity; filter each of theECG sample signals to remove at least a portion of the cardiac signalcomponent and generate a combined myographic power signal for the two ofthe plurality of electrodes with which the ECG sample signal isassociated, wherein each combined myographic power signal represents amyographic potential between the two electrodes; and calculateindividual myographic power signals for each of the plurality ofelectrodes by applying the combined myographic power signals within acovariance matrix; wherein the circuit is configured to: designate oneof the plurality of electrodes to be a reference electrode and theothers of the plurality of electrodes to be recording electrodes; detecta first set of ECG sample signals comprising an ECG sample signal forreach recording electrode with respect to the reference electrode;filter each of the first set of ECG sample signals to generate a firstset of combined myographic power signals, wherein the first set ofcombined myographic power signals comprises a combined myographic powersignal for each recording electrode with respect to the referenceelectrode; and apply the first set of combined myographic power signalswithin the covariance matrix and calculate the individual myographicpower signal for the reference electrode.
 9. The system of claim 8,wherein the individual myographic power signal for the referenceelectrode is calculated based on one or more off-diagonal combinedmyographic power signals within the covariance matrix.
 10. The system ofclaim 8, wherein the circuit is configured to: change the referenceelectrode to another of the plurality of electrode to designate a newreference electrode; designate the electrodes of the plurality ofelectrodes other than the new reference electrode to be new recordingelectrodes; determine a second set of combined myographic power signalscomprising a combined myographic power signal for each new recordingelectrode with respect to the new reference electrode; and calculate theindividual myographic power signal for the new reference electrode byapplying the second set of combined myographic power signals within thecovariance matrix.
 11. The system of 10, wherein the circuit isconfigured to repeat the changing, designating, determining, andcalculating operations until the individual myographic powers signalshave been calculated for each of the plurality of electrodes.
 12. Thesystem of claim 8, wherein the circuit is configured to apply a highpass filter to the ECG sample signals to remove low frequency componentsof the ECG sample signals.
 13. The system of claim 12, wherein thecircuit is configured to: determine a presence of at least one of a QRSwave or a P wave within a portion of the ECG sample signals; and replacethe portion of the ECG sample signals including the at least one of theQRS wave or the P wave with a corresponding portion of the ECG samplesignals from a previously detected sample.
 14. The system of claim 12,wherein the circuit is configured to filter out at least a portion ofthe ECG sample signals corresponding to a frequency of at least one of50 Hz or 60 Hz.
 15. A system for measuring signals representative ofrespiratory activity, the system comprising: a plurality of electrodesconfigured to capture an electrocardiogram (ECG) signal, wherein the ECGsignals comprises a plurality of ECG sample signals, wherein each ECGsample signal is a bipolar signal associated with two of the pluralityof electrodes and comprises a cardiac signal component and a myographicsignal component representative of muscle contractions relating to therespiratory activity; a circuit operably coupled to the plurality ofelectrodes and comprising a microprocessor, wherein the circuit isconfigured to: designate one of the plurality of electrodes to be areference electrode and the others of the plurality of electrodes to berecording electrodes; detect a first set of ECG sample signals withinthe ECG signal, wherein the first set of ECG sample signals comprises anECG sample signal for ach recording electrode with respect to thereference electrode; filter each of the first set of ECG sample signalsto remove at least a portion of the cardiac signal component andgenerate a first set of combined myographic power signals, wherein thefirst set of combined myographic power signals comprises a combinedmyographic power signal for each recording electrode with respect to thereference electrode; apply the first set of combined myographic powersignals within the covariance matrix and calculate the individualmyographic power signal for the reference electrode.
 16. The system ofclaim 15, wherein the individual myographic power signal for thereference electrode is calculated based on one or more off-diagonalcombined myographic power signals within the covariance matrix.
 17. Thesystem of claim 15, wherein the circuit is configured to: change thereference electrode to another of the plurality of electrode todesignate a new reference electrode; designate the electrodes of theplurality of electrodes other than the new reference electrode to be newrecording electrodes; determine a second set of combined myographicpower signals comprising a combined myographic power signal for each newrecording electrode with respect to the new reference electrode; andcalculate the individual myographic power signal for the new referenceelectrode by applying the second set of combined myographic powersignals within the covariance matrix.
 18. The system of 17, wherein thecircuit is configured to repeat the changing, designating, determining,and calculating operations until the individual myographic powerssignals have been calculated for each of the plurality of electrodes.19. The system of claim 15, wherein the circuit is configured to apply ahigh pass filter to the ECG sample signals to remove low frequencycomponents of the ECG sample signals.
 20. The system of claim 19,wherein the circuit is configured to: determine a presence of at leastone of a QRS wave or a P wave within a portion of the ECG samplesignals; and replace the portion of the ECG sample signals including theat least one of the QRS wave or the P wave with a corresponding portionof the ECG sample signals from a previously detected sample.